{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of unique tokens: 12000\n",
      "x_train: (26080, 30) / y_train: (26080, 10)\n",
      "x_test: (6521, 30) / y_test: (6521, 10)\n",
      "number of trained word vector: 17117\n",
      "embedding matrix shape: (12001, 300)\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "import data_loader\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scikitplot as skplt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "tokenized_corpus = 'corpus.tk.txt'\n",
    "trained_word_vector = 'corpus.tk.vec.txt'\n",
    "score_corpus = 'score_corpus.txt'\n",
    "            \n",
    "loader = data_loader.DataLoader(tokenized_corpus, trained_word_vector, score_corpus)\n",
    "loader.load_data()\n",
    "\n",
    "x_train, y_train = loader.train\n",
    "x_test, y_test = loader.test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_9 (InputLayer)            (None, 30)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_7 (Embedding)         (None, 30, 300)      3600300     input_9[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_13 (Conv1D)              (None, 30, 64)       38464       embedding_7[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_14 (Conv1D)              (None, 30, 64)       57664       embedding_7[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_15 (Conv1D)              (None, 30, 64)       76864       embedding_7[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_13 (MaxPooling1D) (None, 10, 64)       0           conv1d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_14 (MaxPooling1D) (None, 10, 64)       0           conv1d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_15 (MaxPooling1D) (None, 10, 64)       0           conv1d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_15 (Dropout)            (None, 10, 64)       0           max_pooling1d_13[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dropout_16 (Dropout)            (None, 10, 64)       0           max_pooling1d_14[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dropout_17 (Dropout)            (None, 10, 64)       0           max_pooling1d_15[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 10, 192)      0           dropout_15[0][0]                 \n",
      "                                                                 dropout_16[0][0]                 \n",
      "                                                                 dropout_17[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "flatten_3 (Flatten)             (None, 1920)         0           concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_18 (Dropout)            (None, 1920)         0           flatten_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 10)           19210       dropout_18[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 3,792,502\n",
      "Trainable params: 192,202\n",
      "Non-trainable params: 3,600,300\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Input, Embedding, Conv1D, MaxPooling1D, Dropout, Concatenate, Flatten, Dense\n",
    "from keras.models import Model\n",
    "from keras.initializers import Constant\n",
    "\n",
    "inputs = Input(shape=(loader.max_corpus_len,), dtype='int32')\n",
    "embedding = Embedding(loader.max_word_num+1, loader.embedding_dim,\n",
    "                     embeddings_initializer = Constant(loader.embedding_matrix),\n",
    "                     input_length=loader.max_corpus_len, trainable=False)(inputs)\n",
    "\n",
    "stacks = []\n",
    "for kernel_size in [2, 3, 4]:\n",
    "    conv = Conv1D(64, kernel_size, padding='same', activation='relu', strides=1)(embedding)\n",
    "    pool = MaxPooling1D(pool_size=3)(conv)\n",
    "    drop = Dropout(0.5)(pool)\n",
    "    stacks.append(drop)\n",
    "    \n",
    "merged = Concatenate()(stacks)\n",
    "flatten = Flatten()(merged)\n",
    "drop = Dropout(0.5)(flatten)\n",
    "outputs = Dense(y_train.shape[1], activation='softmax')(drop)\n",
    "\n",
    "model = Model(inputs, outputs)\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 26080 samples, validate on 6521 samples\n",
      "Epoch 1/30\n",
      " - 12s - loss: 1.2015 - acc: 0.6867 - val_loss: 1.1083 - val_acc: 0.6935\n",
      "Epoch 2/30\n",
      " - 3s - loss: 1.1092 - acc: 0.6931 - val_loss: 1.0874 - val_acc: 0.6930\n",
      "Epoch 3/30\n",
      " - 3s - loss: 1.0885 - acc: 0.6928 - val_loss: 1.0746 - val_acc: 0.6938\n",
      "Epoch 4/30\n",
      " - 3s - loss: 1.0679 - acc: 0.6942 - val_loss: 1.0816 - val_acc: 0.6935\n",
      "Epoch 5/30\n",
      " - 3s - loss: 1.0473 - acc: 0.6948 - val_loss: 1.0659 - val_acc: 0.6938\n",
      "Epoch 6/30\n",
      " - 3s - loss: 1.0388 - acc: 0.6942 - val_loss: 1.0626 - val_acc: 0.6942\n",
      "Epoch 7/30\n",
      " - 3s - loss: 1.0226 - acc: 0.6968 - val_loss: 1.0538 - val_acc: 0.6948\n",
      "Epoch 8/30\n",
      " - 3s - loss: 1.0100 - acc: 0.6967 - val_loss: 1.0522 - val_acc: 0.6951\n",
      "Epoch 9/30\n",
      " - 3s - loss: 1.0006 - acc: 0.6967 - val_loss: 1.0477 - val_acc: 0.6958\n",
      "Epoch 10/30\n",
      " - 3s - loss: 0.9862 - acc: 0.7000 - val_loss: 1.0453 - val_acc: 0.6967\n",
      "Epoch 11/30\n",
      " - 3s - loss: 0.9768 - acc: 0.7012 - val_loss: 1.0656 - val_acc: 0.6950\n",
      "Epoch 12/30\n",
      " - 3s - loss: 0.9634 - acc: 0.7039 - val_loss: 1.0530 - val_acc: 0.6973\n",
      "Epoch 13/30\n",
      " - 3s - loss: 0.9525 - acc: 0.7059 - val_loss: 1.0474 - val_acc: 0.6968\n",
      "Epoch 14/30\n",
      " - 3s - loss: 0.9426 - acc: 0.7068 - val_loss: 1.0608 - val_acc: 0.6994\n",
      "Epoch 15/30\n",
      " - 3s - loss: 0.9370 - acc: 0.7089 - val_loss: 1.0578 - val_acc: 0.6965\n",
      "Epoch 16/30\n",
      " - 3s - loss: 0.9260 - acc: 0.7084 - val_loss: 1.0533 - val_acc: 0.6987\n",
      "Epoch 17/30\n",
      " - 3s - loss: 0.9200 - acc: 0.7113 - val_loss: 1.0505 - val_acc: 0.6981\n",
      "Epoch 18/30\n",
      " - 3s - loss: 0.9084 - acc: 0.7151 - val_loss: 1.0641 - val_acc: 0.6982\n",
      "Epoch 19/30\n",
      " - 3s - loss: 0.9008 - acc: 0.7153 - val_loss: 1.0666 - val_acc: 0.6979\n",
      "Epoch 20/30\n",
      " - 3s - loss: 0.8876 - acc: 0.7184 - val_loss: 1.0684 - val_acc: 0.6971\n",
      "Epoch 21/30\n",
      " - 3s - loss: 0.8787 - acc: 0.7194 - val_loss: 1.0841 - val_acc: 0.6981\n",
      "Epoch 22/30\n",
      " - 3s - loss: 0.8733 - acc: 0.7211 - val_loss: 1.0989 - val_acc: 0.6982\n",
      "Epoch 23/30\n",
      " - 3s - loss: 0.8635 - acc: 0.7238 - val_loss: 1.0887 - val_acc: 0.6979\n",
      "Epoch 24/30\n",
      " - 3s - loss: 0.8619 - acc: 0.7217 - val_loss: 1.0901 - val_acc: 0.6967\n",
      "Epoch 25/30\n",
      " - 3s - loss: 0.8575 - acc: 0.7216 - val_loss: 1.0951 - val_acc: 0.6961\n",
      "Epoch 26/30\n",
      " - 3s - loss: 0.8478 - acc: 0.7263 - val_loss: 1.1141 - val_acc: 0.6974\n",
      "Epoch 27/30\n",
      " - 3s - loss: 0.8362 - acc: 0.7302 - val_loss: 1.1090 - val_acc: 0.6971\n",
      "Epoch 28/30\n",
      " - 3s - loss: 0.8338 - acc: 0.7289 - val_loss: 1.1100 - val_acc: 0.6964\n",
      "Epoch 29/30\n",
      " - 3s - loss: 0.8282 - acc: 0.7284 - val_loss: 1.1184 - val_acc: 0.6979\n",
      "Epoch 30/30\n",
      " - 3s - loss: 0.8208 - acc: 0.7323 - val_loss: 1.1129 - val_acc: 0.6968\n"
     ]
    }
   ],
   "source": [
    "hist = model.fit(x_train, y_train,\n",
    "                epochs=30, batch_size=64,\n",
    "                validation_data=(x_test, y_test), verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcd834b12e8>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred = model.predict(x_test)\n",
    "skplt.metrics.plot_confusion_matrix(np.argmax(y_test, axis=1), np.argmax(pred, axis=1))\n",
    "skplt.metrics.plot_roc(np.argmax(y_test, axis=1), pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize history for accuracy\n",
    "plt.plot(hist.history['acc'])\n",
    "plt.plot(hist.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# summarize history for loss\n",
    "plt.plot(hist.history['loss'])\n",
    "plt.plot(hist.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
